The present disclosure generally relates to monitoring and maintenance planning for turbine engines.
Accurate and cost-effective maintenance planning for turbine engines, such as aircraft engines, or turbines used for propulsion or power generation, requires precise predictions of the lifespan associated with critical degradation and failure modes of engine components. Existing design and maintenance practices focus on collection of operational, damage, and failure data from various sensors and from inspection of the engine as it operates in the field, and comparing the collected data to lifespan predictions made at the design stage. If the field performance data and predictions do not agree, future maintenance planning of the particular engine is based primarily on the collected field data, with a diminished role given to design stage predictions. Previous field experience with an engine having a similar design may also be used to predict an engine component's lifespan. However, a lack of agreement between the predicted lifespan of an engine component and the lifespan observed in the field diminishes the ability of the manufacturer to accurately forecast the actual lifespan of the engine component. As a result, increased cost to the engine manufacturer, reduced availability of the engine, and reduced revenue to the engine owner may occur.
One solution for this problem is diagnostics, prognostics, or health monitoring, which relies primarily on collected sensor data and fusion algorithms to combine the sensor data. Poor sensor performance may yield an inaccurate picture of the state of the engine component. Further, sensor data may not provide a picture of the underlying physics of any degradation that may be occurring in the engine. Data regarding the thermodynamics of the engine's operating cycle may be fused with the collected sensor data; however, the energy balance of the engine may not describe the forces and material response that may drive degradation of the engine component.
Accordingly, there remains a need in the art for a turbine engine component lifespan-prediction method combining design models, remote monitoring and diagnostic (RMD) data, and inspection data into a single, probabilistic, total lifespan predictive model.